Adapting Arts-Based Engagement Ethnography for Different Newcomer Groups
Bibliographic record
Abstract
Background. In 2021, Canada’s newcomer community (individuals who have arrived in Canada as immigrants, refugees, or international students within the last five years) had increased significantly to 1 in 4 people (Immigration, Refugees, and Citizenship Canada, 2023). For many newcomers, schools and communities are their first experience of Canadian culture and the site in which they learn about the norms of their host culture (Areepattamannil & Freeman, 2008; Berry et al., 2006; Rossiter & Rossiter; 2009). Methods. An arts-based engagement ethnography (ABEE) is an innovative, culturally sensitive, and multimodal approach to qualitative research conducted with underrepresented communities (Goopy & Kassan, 2019; Kassan et al., 2020). The intersection of social justice principles and ABEE form a unique research process that is participant-driven and easily adaptable to working with newcomer youth and families, allowing researchers to unearth how newcomers experience integration into Canadian society both individually and collectively. Each participant is given a set of cultural probes (e.g., iPad, diary, maps, stationary, and polaroid camera) and asked to create artifacts that document their integration experiences. The content of participants’ artifacts is used to develop individual interview protocols for each youth or family member, followed by a collective interview through focus groups with students or a family interview. Observations. Results and key learnings from current and past ABEE studies with newcomer youth and families will be presented, including cultural artifacts and integration themes. Conclusion. We present implications for researchers, as well as graduate students, practitioners, and service providers working with newcomer youth and families.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".